Artificial Intelligence (AI) in cybersecurity has become the new normal. However, AI will take some time to win public trust due to inherent biases and adversarial threats ranging from insiders to intrusion and malware. AI-based models can be biased towards the majority class of data on which they are trained due to the imbalance in datasets. Anomaly samples in publicly available datasets are usually scarce compared to the normal data in low data regimes like cybersecurity. Researchers have used data generation techniques like emulation, synthetic oversampling and generative models to mitigate data biasing.
Similarly, adversarial learning has been a topic of pivotal interest to the research communities for the last decade. A plethora of seminal works have been created to deal with adversarial attacks like poisoning, evasion and transferability [1]}, [2]}, [3]}. Adversarial defence strategies can be based on preprocessing, adversarial training, architecture, detection, defensive testing, multiclassifiers and game theory [1]}.
